keras_deployment_to_android.py

# -*- coding: utf-8 -*-
"""keras_deployment_to android.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1OMaYO1wSjrBNNpl5gQ7A-O_5Zq3GcStr
"""

from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import keras
from keras.layers import Activation, Dense, Input
from keras.layers import Conv2D, Flatten
from keras.layers import Reshape, Conv2DTranspose
from keras.models import Model
from keras import backend as K
from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

np.random.seed(1337)

# MNIST dataset
(x_train, _), (x_test, _) = mnist.load_data()

image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

# Generate corrupted MNIST images by adding noise with normal dist
# centered at 0.5 and std=0.5
noise = np.random.normal(loc=0.5, scale=0.5, size=x_train.shape)
x_train_noisy = x_train + noise
noise = np.random.normal(loc=0.5, scale=0.5, size=x_test.shape)
x_test_noisy = x_test + noise

x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)

# Network parameters
input_shape = (image_size, image_size, 1)
batch_size = 128
kernel_size = 3
latent_dim = 16
# Encoder/Decoder number of CNN layers and filters per layer
layer_filters = [32, 64]

# Build the Autoencoder Model
# First build the Encoder Model
inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
# Stack of Conv2D blocks
# Notes:
# 1) Use Batch Normalization before ReLU on deep networks
# 2) Use MaxPooling2D as alternative to strides>1
# - faster but not as good as strides>1
for filters in layer_filters:
    x = Conv2D(filters=filters,
               kernel_size=kernel_size,
               strides=2,
               activation='relu',
               padding='same')(x)

# Shape info needed to build Decoder Model
shape = K.int_shape(x)

# Generate the latent vector
x = Flatten()(x)
latent = Dense(latent_dim, name='latent_vector')(x)

# Instantiate Encoder Model
encoder = Model(inputs, latent, name='encoder')
encoder.summary()

# Build the Decoder Model
latent_inputs = Input(shape=(latent_dim,), name='decoder_input')
x = Dense(shape[1] * shape[2] * shape[3])(latent_inputs)
x = Reshape((shape[1], shape[2], shape[3]))(x)

# Stack of Transposed Conv2D blocks
# Notes:
# 1) Use Batch Normalization before ReLU on deep networks
# 2) Use UpSampling2D as alternative to strides>1
# - faster but not as good as strides>1
for filters in layer_filters[::-1]:
    x = Conv2DTranspose(filters=filters,
                        kernel_size=kernel_size,
                        strides=2,
                        activation='relu',
                        padding='same')(x)

x = Conv2DTranspose(filters=1,
                    kernel_size=kernel_size,
                    padding='same')(x)

outputs = Activation('sigmoid', name='decoder_output')(x)

# Instantiate Decoder Model
decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()

# Autoencoder = Encoder + Decoder
# Instantiate Autoencoder Model
autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder')
autoencoder.summary()

autoencoder.compile(loss='mse', optimizer='adam')

# Train the autoencoder
autoencoder.fit(x_train_noisy,
                x_train,
                validation_data=(x_test_noisy, x_test),
                epochs=30,
                batch_size=batch_size)

# Predict the Autoencoder output from corrupted test images
x_decoded = autoencoder.predict(x_test_noisy)

rows, cols = 10, 30
num = rows * cols
imgs = np.concatenate([x_test[:num], x_test_noisy[:num], x_decoded[:num]])
imgs = imgs.reshape((rows * 3, cols, image_size, image_size))
imgs = np.vstack(np.split(imgs, rows, axis=1))
imgs = imgs.reshape((rows * 3, -1, image_size, image_size))
imgs = np.vstack([np.hstack(i) for i in imgs])
imgs = (imgs * 255).astype(np.uint8)
plt.figure()
plt.axis('off')
plt.title('Original images: top rows, '
          'Corrupted Input: middle rows, '
          'Denoised Input:  third rows')
plt.imshow(imgs, interpolation='none', cmap='gray')
Image.fromarray(imgs).save('corrupted_and_denoised.png')
plt.show()

#Saving the model
autoencoder.save("model.h5")
print("Saved model to disk")

import os

os.listdir()

os.makedirs('./model', exist_ok=True)
autoencoder.save('./model/keras_model.h5')

# In case you ran into the "incompatible with expected resource" issue with a model containing BatchNormization layers such as DenseNet,
#  make sure to set the learning phase to 0 before loading the Keras model in a new session.

from keras import backend as K
K.set_learning_phase(0)

from keras.models import load_model
model = load_model('./model/keras_model.h5')

print(model.output)

print(model.input)

"""As you can see, our simple model has only single input and output, your model might have multiple inputs/outputs.

We keep track of their names since we are going to locate them by name in the converted TensorFlow graph during inference.

The first step is to get the computation graph of TensorFlow backend which represents the Keras model, where the forward pass and training related operations are included.

Then the graph will be converted to a GraphDef protocol buffer, after that it will be pruned so subgraphs that are not necessary to compute the requested outputs such as the training operations are removed. This step if refer to as freezing the graph."""

import tensorflow as tf
import keras.backend as K

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested
    outputs are removed.
    @param session The TensorFlow session to be frozen.
    @param keep_var_names A list of variable names that should not be frozen,
                          or None to freeze all the variables in the graph.
    @param output_names Names of the relevant graph outputs.
    @param clear_devices Remove the device directives from the graph for better portability.
    @return The frozen graph definition.
    """
    from tensorflow.python.framework.graph_util import convert_variables_to_constants
    graph = session.graph
    with graph.as_default():
        freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
        output_names = output_names or []
        output_names += [v.op.name for v in tf.global_variables()]
        
        # Graph -> GraphDef ProtoBuf
        
        input_graph_def = graph.as_graph_def()
        if clear_devices:
            for node in input_graph_def.node:
                node.device = ""
        frozen_graph = convert_variables_to_constants(session, input_graph_def,
                                                      output_names, freeze_var_names)
        return frozen_graph


frozen_graph = freeze_session(K.get_session(),
                              output_names=[out.op.name for out in model.outputs])

#Saving the Keras MOdel (.h5)  as tensorflow (.pb)

tf.train.write_graph(frozen_graph, "model", "tf_model.pb", as_text=False)